CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Data science strategy for injury and violence prevention

Injuries and violence are the leading causes of death in the United States for children, adolescents, and adults ages 18 to 44 years and rank in the top 10 causes of death for persons 45 years or older. In recent years, rates of deaths due to many forms of injury and violence\u2014drug overdose, suicide, homicide, road traffic crashes, and falls\u2014have increased, leading to recent declines in life expectancy in the United States. Beyond rising mortality, injuries and violence contribute to substantial morbidity as well as social and economic costs each year.Preventing injury and violence is a public health imperative given the significant impact on individuals, families, and communities across the United States. However, primary challenges to rapidly addressing these public health problems include limitations of both public health data as well as prevention and response capabilities. Lack of timely information, inability to identify emerging health threats, limited capacity to target services, increasingly prevalent health misinformation, declining participation in and lack of representativeness of traditional data systems, and fragmentation of electronic health records and clinical data systems are examples of the challenges facing contemporary public health efforts.A growing body of research now indicates that application of novel data and data science tools, methods, and techniques can help address critical public health needs, including injury and violence prevention and related issues such as social determinants of health and health equity. Academic research has focused, for example, on the use of novel data sources such as internet search queries to assess disease-related trends in real-time, natural language processing to study electronic health records and other systems with unstructured text, machine learning to improve prevention programming, network analysis to better understand mortality risk, online surveys to improve data timeliness and response rates, and interactive data visualization to improve communication and dissemination of scientific findings.Although data science is an emerging field, academic, industry, and governmental organizations have typically defined it by two consistent features: 1) a multidisciplinary approach that blends methodological techniques from computer science, statistics, and various subject matter domains and 2) a focus on large, complex, or otherwise novel data sources.For the purposes of public health and injury and violence prevention, the National Center for Injury Prevention and Control (Injury Center) defines population-health data science as a multidisciplinary approach combining traditional epidemiologic methods and contemporary computer science techniques, with a particular focus on large and complex data sources, to improve the measurement and prevention of injury and violence in communities.Suggested Citation: Centers for Disease Control and Prevention. Data Science Strategy for Injury and Violence Prevention. Atlanta, GA: National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, 2020.Data-Science-Strategy_FINAL_508.pdf20201140

Doctor of Philosophy

dissertationClinical decision support systems (CDSS) and electronic health records (EHR) have been widely adopted but do not support a high level of reasoning for the clinician. As a result, workflow incongruity and provider frustrations lead to more errors in reasoning. Other successful fields such as defense, aviation, and the military have used task complexity as a key factor in decision support system development. Task complexity arises during the interaction of the user and the tasks. Therefore, in this dissertation I have utilized different human factor methods to explore task complexity factors to understand their utility in health information technology system design. The first study addresses the question of generalizing complexity through a clinical complexity model. In this study, we integrated and validated a patient and task complexity model into a clinical complexity model tailored towards healthcare to serve as the initial framework for data analysis in our subsequent studies. The second study addresses the question of the coping strategies of infectious disease (ID) clinicians while dealing with complex decision tasks. The study concluded that clinicians use multiple cognitive strategies that help them to switch between automatic cognitive processes and analytical processes. The third study identified the complexity contributing factors from the transcripts of the observations conducted in the ID domain. The clinical complexity model developed in the first study guided the research for identifying the prominent complexity iv factors to recommend innovative healthcare technology system design. The fourth study, a pilot exploratory study, demonstrated the feasibility of developing a population information display from querying real complex patient information from an actual clinical database as well as identifying the ideal features of population information display. In summary, this dissertation adds to the knowledge about how clinicians adapt their information environment to deal with complexity. First, it contributes by developing a clinical complexity model that integrates both patient and task complexity. Second, it provides specific design recommendations for future innovative health information technology systems. Last, this dissertation also suggests that understanding task complexity in the healthcare team domain may help to better design of interface system

Preserving user privacy in social media data processing

Social media data is used for analytics, e.g., in science, authorities or the industry. Privacy is often considered a secondary problem. However, protecting the privacy of social media users is demanded by laws and ethics. In order to prevent subsequent abuse, theft or public exposure of collected datasets, privacy-aware data processing is crucial. This dissertation presents a concept to process social media data with social media user’s privacy in mind. It features a data storage concept based on the cardinality estimator HyperLogLog to store social media data, so that it is not possible to extract individual items from it, but only to estimate the cardinality of items within a certain set, plus running set operations over multiple sets to extend analytical ranges. Applying this method requires to define the scope of the result before even gathering the data. This prevents the data from being misused for other purposes at a later point in time and thus follows the privacy by design principles. This work further shows methods to increase privacy through the implementation of abstraction layers. An included case study demonstrates the presented methods to be suitable for application in the field.:1 Introduction 1.1 Problem 1.2 Research objectives 1.3 Document structure 2 Related work 2.1 The notion of privacy 2.2 Privacy by design 2.3 Differential privacy 2.4 Geoprivacy 2.5 Probabilistic Data Structures 3 Concept and methods 3.1 Collateral data 3.2 Disposable data 3.3 Cardinality estimation 3.4 Data precision 3.5 Extendability 3.6 Abstraction 3.7 Time consideration 4 Summary of publications 4.1 HyperLogLog Introduction 4.2 VOST Case Study 4.3 Real-time Streaming 4.4 Abstraction Layers 4.5 VGIscience Book Chapter 4.6 Supplementary Software Materials 5 Discussion 5.1 Prevent accidental data disclosure 5.2 Feasibility in the field 5.3 Adjustability for different use cases 5.4 Limitations of HLL 5.5 Security 5.6 Outlook and further research 6 Conclusion Appendix References Publication

A workload‑driven approach for view selection in large dimensional datasets

The information explosion the world has witnessed in the last two decades has forced businesses to adopt a data-driven culture for them to be competitive. These data-driven businesses have access to countless sources of information, and face the challenge of making sense of overwhelming amounts of data in a efficient and reliable manner, which implies the execution of read-intensive operations. In the context of this challenge, a framework for the dynamic read-optimization of large dimensional datasets has been designed, and on top of it a workload-driven mechanism for automatic materialized view selection and creation has been developed. This paper presents an extensive description of this mechanism, along with a proof-of-concept implementation of it and its corresponding performance evaluation. Results show that the proposed mechanism is able to derive a limited but comprehensive set of views leading to a drop in query latency ranging from 80% to 99.99% at the expense of 13% of the disk space used by the base dataset. This way, the devised mechanism enables speeding up query execution by building materialized views that match the actual demand of query workloads

Contextual Social Networking

The thesis centers around the multi-faceted research question of how contexts may be detected and derived that can be used for new context aware Social Networking services and for improving the usefulness of existing Social Networking services, giving rise to the notion of Contextual Social Networking. In a first foundational part, we characterize the closely related fields of Contextual-, Mobile-, and Decentralized Social Networking using different methods and focusing on different detailed aspects. A second part focuses on the question of how short-term and long-term social contexts as especially interesting forms of context for Social Networking may be derived. We focus on NLP based methods for the characterization of social relations as a typical form of long-term social contexts and on Mobile Social Signal Processing methods for deriving short-term social contexts on the basis of geometry of interaction and audio. We furthermore investigate, how personal social agents may combine such social context elements on various levels of abstraction. The third part discusses new and improved context aware Social Networking service concepts. We investigate special forms of awareness services, new forms of social information retrieval, social recommender systems, context aware privacy concepts and services and platforms supporting Open Innovation and creative processes. This version of the thesis does not contain the included publications because of copyrights of the journals etc. Contact in terms of the version with all included publications: Georg Groh, [email protected] zentrale Gegenstand der vorliegenden Arbeit ist die vielschichtige Frage, wie Kontexte detektiert und abgeleitet werden können, die dazu dienen können, neuartige kontextbewusste Social Networking Dienste zu schaffen und bestehende Dienste in ihrem Nutzwert zu verbessern. Die (noch nicht abgeschlossene) erfolgreiche Umsetzung dieses Programmes führt auf ein Konzept, das man als Contextual Social Networking bezeichnen kann. In einem grundlegenden ersten Teil werden die eng zusammenhängenden Gebiete Contextual Social Networking, Mobile Social Networking und Decentralized Social Networking mit verschiedenen Methoden und unter Fokussierung auf verschiedene Detail-Aspekte näher beleuchtet und in Zusammenhang gesetzt. Ein zweiter Teil behandelt die Frage, wie soziale Kurzzeit- und Langzeit-Kontexte als für das Social Networking besonders interessante Formen von Kontext gemessen und abgeleitet werden können. Ein Fokus liegt hierbei auf NLP Methoden zur Charakterisierung sozialer Beziehungen als einer typischen Form von sozialem Langzeit-Kontext. Ein weiterer Schwerpunkt liegt auf Methoden aus dem Mobile Social Signal Processing zur Ableitung sinnvoller sozialer Kurzzeit-Kontexte auf der Basis von Interaktionsgeometrien und Audio-Daten. Es wird ferner untersucht, wie persönliche soziale Agenten Kontext-Elemente verschiedener Abstraktionsgrade miteinander kombinieren können. Der dritte Teil behandelt neuartige und verbesserte Konzepte für kontextbewusste Social Networking Dienste. Es werden spezielle Formen von Awareness Diensten, neue Formen von sozialem Information Retrieval, Konzepte für kontextbewusstes Privacy Management und Dienste und Plattformen zur Unterstützung von Open Innovation und Kreativität untersucht und vorgestellt. Diese Version der Habilitationsschrift enthält die inkludierten Publikationen zurVermeidung von Copyright-Verletzungen auf Seiten der Journals u.a. nicht. Kontakt in Bezug auf die Version mit allen inkludierten Publikationen: Georg Groh, [email protected]